| Full text | |
| Author(s): |
Pimenta, Guilherme B. A.
;
Dallaqua, Fernanda B. J. R.
;
Fazenda, Alvaro
;
Faria, Fabio A.
;
DeCarvalho, BM
;
Goncalves, LMG
Total Authors: 6
|
| Document type: | Journal article |
| Source: | 2022 35TH SIBGRAPI CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI 2022); v. N/A, p. 6-pg., 2022-01-01. |
| Abstract | |
Tropical forests represent the home of many species on the planet for flora and fauna, retaining billions of tons of carbon footprint, promoting clouds and rain formation, implying a crucial role in the global ecosystem, besides representing the home to countless indigenous peoples. Unfortunately, millions of hectares of tropical forests are lost every year due to deforestation or degradation. To mitigate that fact, monitoring and deforestation detection programs are in use, in addition to public policies for the prevention and punishment of criminals. These monitoring/detection programs generally use remote sensing images, image processing techniques, machine learning methods, and expert photointerpretation to analyze, identify and quantify possible changes in forest cover. Several projects have proposed different computational approaches, tools, and models to efficiently identify recent deforestation areas, improving deforestation monitoring programs in tropical forests. In this sense, this paper proposes the use of pattern classifiers based on neuroevolution technique (NEAT) in tropical forest deforestation detection tasks. Furthermore, a novel framework called e-NEAT has been created and achieved classification results above 90% for balanced accuracy measure in the target application using an extremely reduced and limited training set for learning the classification models. These results represent a relative gain of 6.2% over the best baseline ensemble method compared in this paper. (AU) | |
| FAPESP's process: | 19/26702-8 - Trends on high performance computing, from resource management to new computer architectures |
| Grantee: | Alfredo Goldman vel Lejbman |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 15/24485-9 - Future internet for smart cities |
| Grantee: | Fabio Kon |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 14/50937-1 - INCT 2014: on the Internet of the Future |
| Grantee: | Fabio Kon |
| Support Opportunities: | Research Projects - Thematic Grants |
| FAPESP's process: | 18/23908-1 - Towards the Robustness in Deep Learning Architectures for e-Science Applications |
| Grantee: | Fabio Augusto Faria |
| Support Opportunities: | Scholarships abroad - Research |
| FAPESP's process: | 17/25908-6 - Weakly supervised learning for compressed video analysis on retrieval and classification tasks for visual alert |
| Grantee: | João Paulo Papa |
| Support Opportunities: | Research Grants - Research Partnership for Technological Innovation - PITE |